Acadian Starts Experimenting with Bing on Predictive Investing

The future of predictive investing may have taken a step
forward based on a new partnership between Acadian XX and Bing Predicts, the search
software developed by Microsoft, to make connections between macroeconomic
events and market behavior.

In an interview, John Chisholm, chief investment officer of
Acadian, said the current effort is “a research project for now, rather than any definite
enhancement to our investment process.” Historically, Acadian “has used
data to make objective investment decisions…we are excited about the ways that
new technology can help draw insight from large information sets.”

Acadian chose Bing Predicts because it uses machine learning
from data that is trending on social media topics, supplemented by sentiment
toward those topics plus other trending searches on Bing. This combination may
be useful to investment managers looking for various factors, including building
sentiment, determining sentiment, and gathering information about an industry,
company or macroeconomic event.

Among its possible applications is “to define some reliable,
persistent predictive measures from the Bing search data. There are promising
areas of focus, including sentiment and industry level signals,” Chisholm
said.

He also said “our first step is to explore the data, and
then we will formulate some test measures, and then go through a series of
experiments to see if adding these predictive measures to our investment model
would have improved our results. We will actually employ the Bing signals only
if we see a robust outcome from this research.”

The Hope for Predicting
Positive Sentiment

This developing area of taking user-generated content from
social media outlets is ripe for analysis and data mining. Already, it has been
used to predict elections, monitoring brands and in disaster management.

Cliff Moyce, Global Head of DataArt’s Finance Practice in
London, said “searching for non-intuitive insights, especially those with weak
but real correlations to performance outcomes, is a well-known (but not that
well-practiced) investment management strategy. Firms like Winton Capital do it
very well.”

Moyce, who has worked on projects involving the automation
of trading at Europe’s largest derivatives exchange and building London’s first
automated equity index arbitrage system for equities, added that “what machine
learning does is increase massively the amount of data, especially unstructured
data that it can handle efficiently and effectively. Bing Predicts brings that
capability to firms in a ready-made form, thus reducing the amount of in-house
investment needed. The decision by Acadian is a significant step to machine
learning becoming a standard investment management tool.”

In a 2017
paper discussing social media analysis, the authors used software that
analyzed sentences to determine the writer’s sentiment (pro or con), polarity
(degree of favorability) and targets (whether they are a person, product or
service). Their study includes Tweets, but those pose
problems because they are created almost immediately,
and are often in slang “so their form is less standard and they contain many
more spelling errors, slang and other out-of-vocabulary words” that make
analysis more difficult.

In one area that may be most related
to the Bing project, the authors found that sentiment analysis using social
media “can capture large-scale trends using the large amount of data generated
by people.” In one research paper, the researchers used consumer opinions from
microblogs concerning various brands and found that 19% of microblog messages
contain the mention of a brand and 20% of these contain sentiments related to
the brand. “Monitoring these sentiments allows companies to gain insights into
the positive and negative aspects of their products.”

In practice, Bing Predicts has been
used to predict the weather and even who would win the American Idol talent
contest. It does this by using a
prediction engine combined with machine learning models “to infer outcomes on
several events, starting with television shows,” Sun recalled. “We tried
multiple features in our models and the best performing algorithms on the
features we used ended up being similar to our Bing search ranking models. In
particular, for voting shows, our search machine learning models did a good job
in predicting the ranks of show participants,” according to Walter
Sun, Development Manager
for the Core Ranking team at Bing.

While this works for game shows and
elections, Acadian is applying it to macroeconomic events. This may help
explain why Chisolm said Acadian is “excited about the ways
that new technology can help draw insight from large information sets. Watch
this space!”

More AI Applications Being
Developed

Other
major search engines, such as Google, Bing, Yahoo, and Yandex have various search
features, but Bing is more customizable and better suited to handle social
media inputs. Like other search engines, search engine optimization (SEO) is
not only about keywords, links, and content, but has other key features.

In the past decade, Artificial Intelligence (AI) has moved
from algorithms using the limited inputs of “if-and” statements to algorithms
that can learn using larges data sets ,and applications for the emerging world
of neuro-finance. This
evolution can be summed up as moving from algorithms built on decision tree, ”if-then” logic to neural networks
already trained on historical data to networks that are time adjusted. The next
breakthrough should be algorithms that allow neural networks to be retrained in
real time.

In
addition to Bing Predicts in the investment management industry, AI
applications are being deployed in industries such as marketing, retail
and entertainment. IBM’s Watson, for example, is being used to help customers
chose diamond rings; pay their taxes; help environmentalists find the best
technologies to treat contaminated sites; help children under age 13
communicate and develop their speaking and writing abilities; and is helping TV
writers develop characters based on finding common denominators in people’s individual
Twitter profiles.